Summary: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals.
Parameter | Value |
---|---|
Name | Robot Execution Failures |
Labeled | Yes |
Time Series | Yes |
Simulation | No |
Missing Values | No |
Dataset Characteristics | Multivariate, Time-Series |
Feature Type | Integer |
Associated Tasks | Classification |
Number of Instances | 463 |
Number of Features | 90 |
Date Donated | 1999-04-22 |
Source | UCI Machine Learning Repository |
The donation includes 5 datasets, each of them defining a different learning problem:
* LP1: failures in approach to grasp position
* LP2: failures in transfer of a part
* LP3: position of part after a transfer failure
* LP4: failures in approach to ungrasp position
* LP5: failures in motion with part
In order to improve classification accuracy, a set of five feature transformation strategies (based on statistical summary features, discrete Fourier transform, etc.) was defined and evaluated. This enabled an average improvement of 20% in accuracy. The most accessible reference is [Seabra Lopes and Camarinha-Matos, 1998].
Robotics, Failure detection, Force and torque data, Time-series analysis, Machine learning